ce_delta_sum = [1, 1]
while max(ce_delta_sum) > 0.001 * sources_number * cases_number:
cef_for_each_s = []
observed_cases_changed = [] + observed_cases
for observed_case_index in range(cases_number):
observed_cases_changed[observed_case_index].update({"life_span": set_of_life_spans[observed_case_index]})
observed_cases_changed = cook_raw_value(observed_cases_changed)
time_points = observed_cases_changed[0].get("life_span")[0]
for s in observed_keys:
life_span_set = []
sources_data = []
time_points = []
for case in raw_cases:
sources_data.append(case.get(s))
cef = get_CEF(life_span_set=set_of_life_spans, sources_data=sources_data)
cef_measures.update({s: cef})
cef_for_each_s.append(cef)
set_of_life_spans = []
for observed_changed in observed_cases_changed:
del observed_changed["life_span"]
life_span = get_life_span(observed=observed_changed, cef_measures=cef_measures)
set_of_life_spans.append(life_span)
ce_delta_sum = [0, 0]
for old, new in zip(cef_for_each_s_old, cef_for_each_s):
diff_for_s = [abs(x - y) for x, y in zip(old[0:2], new[0:2])]
for i in range(len(ce_delta_sum)):
ce_delta_sum[i] += diff_for_s[i]
cef_for_each_s_old = cef_for_each_s
print 'CASE NUMBER: {}'.format(case_number)
print 'Ground truth: {}'.format(ground_truth)
for key in observed_keys:
print '{}: {}'.format(key, observed.get(key)[1])
print 'Initial life span: {}'.format(life_span)
life_span_old = []
cef_for_each_s_old = [cef_measures.get(s) for s in observed_keys]
ce_delta_sum = [1, 1]
while max(ce_delta_sum) > 0.01*sources_number:
cef_for_each_s = []
observed_changed = observed.copy()
observed_changed.update({'life_span': life_span})
observed_changed = cook_raw_value([observed_changed])[0]
for s in observed_keys:
cef = get_CEF(life_span=observed_changed.get('life_span'), source_data=observed_changed.get(s))
cef_measures.update({s: cef})
cef_for_each_s.append(cef)
life_span_old = life_span
life_span = get_life_span(observed=observed, cef_measures=cef_measures)
iter_quantity += 1
ce_delta_sum = [0, 0]
for old, new in zip(cef_for_each_s_old, cef_for_each_s):
diff_for_s = [abs(x-y) for x, y in zip(old[0:2], new[0:2])]
for i in range(len(ce_delta_sum)):
ce_delta_sum[i] += diff_for_s[i]
cef_for_each_s_old = cef_for_each_s
majority_voting_result = majority_voting(observed)
print 'CASE NUMBER: {}'.format(case_number)
print 'Ground truth: {}'.format(ground_truth)
for key in observed_keys:
print '{}: {}'.format(key, observed.get(key)[1])
print 'Initial life span: {}'.format(life_span)
life_span_old = []
cef_for_each_s_old = [cef_measures.get(s) for s in observed_keys]
ce_delta_sum = [1, 1]
while max(ce_delta_sum) > 0.01 * sources_number:
cef_for_each_s = []
observed_changed = observed.copy()
observed_changed.update({'life_span': life_span})
observed_changed = cook_raw_value([observed_changed])[0]
for s in observed_keys:
cef = get_CEF(life_span=observed_changed.get('life_span'),
source_data=observed_changed.get(s))
cef_measures.update({s: cef})
cef_for_each_s.append(cef)
life_span_old = life_span
life_span = get_life_span(observed=observed,
cef_measures=cef_measures)
iter_quantity += 1
ce_delta_sum = [0, 0]
for old, new in zip(cef_for_each_s_old, cef_for_each_s):
diff_for_s = [abs(x - y) for x, y in zip(old[0:2], new[0:2])]
for i in range(len(ce_delta_sum)):
ce_delta_sum[i] += diff_for_s[i]
cef_for_each_s_old = cef_for_each_s
majority_voting_result = majority_voting(observed)